Better buyer personas with predictive analytics

You pay your marketing agency a hefty sum to come up with buyer personas to advertise your products. You create TV adverts to target these personas. Total spend? Millions of dollars. Expected return? Unknown.

They pay marketing teams and agencies huge sums of money to buyer personas. Heck, they might even spend hours debating about different details on them. But the result is often no better than a semi-informed shot in a dark.

Why is that? What can you do about it?

What is a buyer persona?

But first, let's talk about what a buyer persona (or customer persona) is. Here's a definition from Hubspot:

A buyer persona is a semi-fictional representation of your ideal customer based on market research and real data about your existing customers.

Buyer personas provide tremendous structure and insight for your company. A detailed buyer persona will help you determine where to focus your time, guide product development, and allow for alignment across the organization. As a result, you will be able to attract the most valuable visitors, leads, and customers to your business.

What's wrong with this approach?

Most senior marketing executives and CMOs come up with customer personas with a mix of gut feel from touch points with customers, research reports and creative agencies.

This is a top-down approach that middle managers then find data to support. There is an inherent bias to these decisions and, as a result, they often deviate from the truth.

Should big brands be comfortable making multi-million dollar marketing decisions based on an ideal customer that is not truly backed by science?

Probably not.

Enter predictive buyer personas

At Metisa, we approach the problem from the opposite angle. We take a data-centric, bottom-up approach to figuring out who customer personas should be.

We group existing customers with similar buying patterns and interests and figure out which way of grouping them produces the best fit.

Combining this with our predicted customer lifetime value analysis, we know how valuable each of the segments is as well as how valuable a customer in each segment is.

We know who each of the customers in each segment is, so knowing their age and gender distributions is trivial.

Using a bit of natural language processing, we also know what keywords customers in each segment are interested in, which could be used to figure out their "motivations and goals":

We also know what sort of products they buy:

We think that this approach, combined with the traditional one, is probably a much more objective and data-driven way for brands to determine their customer personas and where to spend their marketing budgets.